PROJECTSUMMARY/ABSTRACT Cellularstatetransitions(i.e.frompluripotenttocommitted,replicativetoquiescent,etc.)requirethe coordinatedregulationofthousandsofgenes.Therapeuticallyharnessingthesetransitionsholdsgreat promiseforhumanhealth;?forinstance,autologousstemcelltherapyhasbeensuccessfullyusedin regenerativemedicineandcancertreatments,amongothers.Whilesomeofthekeyregulatoryswitchesare known,thefieldlacksasystems-levelunderstandingofthegenomiccircuitsthatcontrolthesetransitions, informationthatiscriticalforinformedclinicalintervention.Here,wewilldevelopanintegratedcomputational frameworktoidentifycoregeneregulatorycircuitsfromlargegenenetworksandpredicttheirdynamicsand regulatoryfunctionswithouttheneedofdetailednetworkkineticparameters.Advancesingenomicsprofiling technologyhaveenabledthemappingofgeneregulatorynetworks,thuswenowhavethecapacitytoidentify combinatorialinteractionsamonggenesandthemasterregulatorsofstatetransitions.Somesystemsbiology approacheshavesimulatedthedynamicsofageneregulatorycircuit,buttraditionalmethodssufferfromtwo keyissues.First,thereisnorationalruletochooseanappropriatesetofregulatorgenesinalargenetworkto model.Second,sinceitishardtodirectlymeasuremostnetworkkineticparametersfromexperiment, modelingresultsarebasedonasetofguessedparametersthatcanbelessthanoptimal,limitingthe applicationofmathematicalmodelingtolargesystemsandthepredictionpowerofsystemsbiology.To addresstheseissues,werecentlydevelopedalgorithmnamedrandomcircuitperturbation(RACIPE).RACIPE generatesanensembleofcircuitmodels,eachofwhichcorrespondstoadistinctsetofrandomkinetic parameters,anduniquelyidentifiesrobustfeatures,suchasclustersofstablegeneexpressionstates,by statisticalanalysis.WewillfurtherenhanceRACIPEalgorithmsforlargesystemsandnewdataanalysistools usingmachinelearning.Thisapproachwillconvertatraditionalnonlineardynamicsproblemintoadata analysisproblem,anessentialstepforextendingtheapplicationofgenecircuitmodelingtolargesystems.It alsoprovidesanovelstrategytointegratetop-downgenomicsapproacheswithbottom-upmathematical modeling.Thealgorithmswillbetestedandrefinedusingliterature-basedgenenetworks,publicgenomics data,anddatafromcollaboration,withafocusoncelldifferentiationindevelopmentalprocessesandstate transitionsinoncogenesis.Successoftheprojectwillresultinacomprehensivetoolkitthatwillunveilthegene regulatorymechanismofcellulardecision-makinginanycellofinterest.Thealgorithmicdevelopmentis expectedtohaveabroadimpactonnotonlybasicresearchinsystemsbiologybutalsoshedlighton therapeuticinterventioningenomicmedicine.